IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 6, NO. 2, APRIL 2013

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Cross-Calibration for Data Fusion of EO-1/Hyperion
and Terra/ASTER
Naoto Yokoya, Norimasa Mayumi, and Akira Iwasaki

Abstract—The data fusion of low spatial-resolution hyperspectral and high spatial-resolution multispectral images enables the
production of high spatial-resolution hyperspectral data withsmall spectral distortion. EO-1/Hyperion is the world’s first hyperspectral sensor. It was launched in 2001 and has a similar orbit
to Terra/ASTER. In this work, we apply hyperspectral and multispectral data fusion to EO-1/Hyperion and Terra/ASTER datasetsby the preprocessing of datasets and the onboard cross-calibrationof sensor characteristics. The relationship of the spectral response
function is determined by convex optimization by comparing
hyperspectral and multispectral images over the same spectral
range. After accurate image registration, the relationship of
the point spread function is obtained by estimating a matrix
that acts as Gaussian blur filter between two images. Two pansharpening-based methods and one unmixing-based method are
adopted for hyperspectral and multispectral data fusion and theirproperties are investigated.
Index Terms—Cross-calibration, data fusion, hyperspectral,
multispectral.

I. INTRODUCTIONHYPERION [1] and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) [2] data are
currently available as real satellite hyperspectral and multispectral datasets. Hyperion is a hyperspectral imager onboard theEarth Observing 1 (EO-1) satellite launched on 21 November
2000. The Hyperion system, consisting of visible near-infrared
radiometer (VNIR) and shortwave infrared radiometer (SWIR)
subsystems, acquires data in 198 spectral bands with 30 m
ground sampling distance. The Hyperion VNIR obtains opticalimages in 50 bands over a range of 427–925 nm, and the full
width at half maximum (FWHM) of the spectral response function is 10.2 nm. Owing to this narrow spectral width, Hyperion
can be used for spectral cross-calibration with a multispectral
sensor. ASTER is a multispectral imager on the Terra platform,
which was launched on 18 December 1999. ASTER consists
Manuscript received February 14, 2012; revised June 21, 2012; accepted July05, 2012. Date of publication October 18, 2012; date of current version May 13,
2013. This project was partly carried out under a contract with AIST (National
Institute of Advanced Industrial Science and Technology).
N. Yokoya is with the Department of Aeronautics and Astronautics, University of Tokyo, Tokyo 153-0041, Japan (corresponding author, e-mail:yokoya@sal.rcast.u-tokyo.ac.jp).
N. Mayumi is with the Department of Aeronautics and Astronautics, University of Tokyo, Tokyo 153-0041, Japan (e-mail: mayu@sal.rcast.u-tokyo.ac.jp).
A. Iwasaki is with the Department of Advanced Interdisciplinary Studies,
University of Tokyo, Tokyo, Japan (e-mail: aiwasaki@sal.rcast.u-tokyo.ac.jp).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/JSTARS.2012.2208449

of a VNIR, a SWIR, and a thermal infrared radiometer (TIR)with 14 spectral bands. The ASTER VNIR has three spectral
channels over a spectral range of 520–860 nm with 15 m groundsampling distance. The Hyperion/VNIR and ASTER/VNIR
datasets are good examples of hyperspectral and multispectral
datasets with trade-offs between spatial and spectral resolution.
Hyperspectral imaging sensors generally have a larger groundsampling distance than multispectral imaging sensors. The data
fusion of low spatial-resolution hyperspectral and high spatialresolution multispectral images can produce high spatial-resolution hyperspectral data with small spectral distortion [3]–[7].
The fused data enable accurate classification with a fine spatial resolution and thereby enhance the applications of hyperspectral remote sensing, e.g., urban classification and tree-level
forest monitoring [8], [9] . There are many studies on pansharpening techniques, which enhance the spatial resolution of multispectral data using panchromatic data [10]–[13]. Comparedwith pansharpening, hyperspectral and multispectral data fusion
is a new technology. Several hyperspectral and multispectral fusion methods have been proposed that use pansharpening techniques, a stochastic method, and unmixing [3]–[7]. Pansharpening based methods such as smoothing filter-based intensity
modulation (SFIM) [10] (called the blur transform in [7]) andspectral simulation color normalization (SSCN) have produced
effective results in the common spectral regions of two sensors [7]. Since hyperspectral images and the multispectral images in the corresponding band are highly correlated, the spectral distortion of fused data becomes small. This is different
from the case of pansharpening, i.e., the fusion of multispectral and panchromatic images. A maximum a posteriori estimation method was developed to enhance the spatial resolutionof hyperspectral data using higher spatial-resolution data suchas multispectral and panchromatic images [5]. An unmixingbased fusion method, coupled nonnegative matrix factorization(CNMF), was recently proposed to enhance the spatial resolution of all hyperspectral bands [6]. In many studies, data fusion
methods have only been evaluated using synthetic datasets generated from original high spatial-resolution hyperspectral datausing simple sensor characteristics. For practical use, methodsof applying hyperspectral and multispectral data fusion to a real
dataset including the preprocessing and estimation of sensor
characteristics need to be established.
In the application of real data, the preprocessing of datasetsand the onboard cross-calibration of the characteristics (point
spread function and spectral response function) of hyperspectral
and multispectral sensors are necessary for accurate data fusion.
Image registration and radiometric correction are considered as
preprocessing techniques. In this work, we apply hyperspectral
and multispectral data fusion to Hyperion and ASTER datasets.

1939-1404/$31.00 © 2012 IEEE

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A method for the cross-calibration of spatial and spectral characteristics is proposed. SFIM, SSCN, and CNMF are adopted
as the fusion methods and their properties are investigated.
This paper is organized as follows. In Section II we describe
the SFIM, SSCN, and CNMF data fusion methods. In Section III
we present preprocessing and estimation methods to determine
the relationships between sensor characteristics in both spatialand spectral domains. In Section IV we describe experimental
data and evaluation metric. Experimental results and a discussion are presented in Section V, and the paper is concluded in
Section VI.
II. DATA FUSION METHODS
The aim of hyperspectral and multispectral data fusion isto estimate unobservable high spatial-resolution hyperspectral
data () from observed low spatial-resolution hyperspectral data () and high spatial-resolution
multispectral data ().
anddenote thenumbers of spectral channels of hyperspectral and multispectral
sensors, respectively.
anddenote the numbers of pixels
in hyperspectral and multispectral images, respectively. Owing
to the trade-off between the spectral and spatial resolution of
the two sensors,
andare satisfied. Alldata are expressed in matrix form with each column vector representing the spectrum at each pixel. The two observed datasets
are assumed to be obtained under the same atmospheric and illumination conditions, and to be geometrically co-registered witha radiometric correction.
Hyperspectral and multispectral images can be considered
as degraded versions of a high spatial-resolution hyperspectral
image in the spatial and spectral domains, respectively. Whenthese degradations are assumed to be linear operations, andare modeled as(1)(2)
is the point spread transform matrix, whichHere,acts as a low-pass filter. Each column vector
represents the transform of the point spread function from themultispectral image to the
pixel value in the hyperspectral.
image and is assumed to be normalized, i.e.,
is the spectral response transform matrix witheach row vector
representing the transform of
the spectral response function from the hyperspectral sensor totheband detector in the multispectral sensor.
andareresiduals. Methods of estimating and are given in the next
section. In this section, we introduce hyperspectral and multispectral data fusion methods based on pan-sharpening and unmixing.
A. Pansharpening-Based Method
Pansharpening is a technique for enhancing the spatial resolution of a multispectral image by using a panchromatic image.
It is applicable to hyperspectral and multispectral data fusion
over the overlapping spectral bands [7]. In this section, we introduce two pansharpening-based methods for hyperspectral andmultispectral data fusion, i.e., SFIM and SSCN, because they

produce good results compared with the commonly used andstate-of-the-art pansharpening methods despite their simple implementation [7].
SFIM assumes that the radiance ratio between high spatial-resolution multispectral and original panchromatic images
is the same as that between original multispectral and low-passfiltered panchromatic images based on a simplified model forsolar radiation and land surface reflection [10]. When applied
to hyperspectral and multispectral data fusion, the relationship between multispectral and panchromatic images can be
replaced with that between hyperspectral and multispectral
images. Therefore, the() pixel valueof the high spatial-resolution hyperspectral image in the() band is given by
(3)
where
is the low spatial-resolution multispectral image, is the index of the multispectral band that covers
the spectral range of thehyperspectral band, and () is the pixel index in the low spatial-resolution
image corresponding to () in the high spatial-resolution image. The low spatial-resolution multispectral
image can be given by a low-pass filtered version of the original multispectral image:(4)
Substitutingforin (3), in SFIM the() pixel value of the high spatial-resolution hyperspectral image in the() band is calculated
as(5)

SSCN is an extension of the color normalization (CN) method[4]. It is also based on the assumption of (3), and the low spatialresolution multispectral image () is assumed tobe written as a linear combination of the original hyperspectral
images. The spectral response functions of two sensors are taken
into account in the coefficients used in the linear combination
instead of the simple averaging used in the CN method. The
low spatial-resolution multispectral image can be given by(6)This fundamental assumption of the SSCN method is valid
for hyperspectral and multispectral datasets owing to thesufficiently narrow hyperspectral bandwidth. By substitutingforin (3), the() pixel valueof the high spatial-resolution hyperspectral image in the() band is obtained as(7)
B. Unmixing-Based Method
The CNMF method based on linear unmixing was proposed
for hyperspectral and multispectral data fusion [6]. This method

YOKOYA et al.: CROSS-CALIBRATION FOR DATA FUSION OF EO-1/HYPERION AND TERRA/ASTER

gives good results for synthetic hyperspectral and multispectral
datasets with a six fold space difference in the ground sampling
distance. Over the last decade, nonnegative matrix factorization(NMF) [14], [15] has emerged as a useful unmixing method[16]–[20]. The CNMF method is composed of alternate NMF
unmixings of hyperspectral and multispectral images, which areused to extract high spectral-resolution endmember spectra andhigh spatial-resolution abundance maps.
In a linear spectral mixture model, the spectrum at each pixel
is assumed to be a linear combination of several endmember
spectra. Therefore, can be formulated as
(8)
where
is the spectral signature matrix with eachcolumn vector representing an endmember spectrum, and is
the number of endmembers.
is the abundance matrix, with each column vector denoting the abundance fractionsof all endmembers at the pixel, andis the noise.
The endmember spectra and abundances are nonnegative. In addition, the sum of the abundances for each pixel can be assumed
to be unity, i.e.,().
The spatially degraded abundance matrix
andthe spectrally degraded endmember matrix
aredefined asand, respectively. By substituting (8) into (1) and (2), and can be approximated as(9)(10)
Owing to the nonnegative characteristics of , ,, and ,
all the components ofandare also nonnegative. The
CNMF algorithm starts from the NMF unmixing of a hyperspectral image ( ) in order to use its spectral advantage. Hyperspectral ( ) and multispectral ( ) images are alternately unmixed
by NMF using
andfor the initialization ofand, respectively. The high spatial-resolution
hyperspectral image is obtained by the multiplication ofand. To satisfy the abundance sum-to-one constraint, a method in[21] is used. More details concerning CNMF are given in [6].
III. PREPROCESSING AND CROSS-CALIBRATION OF SENSOR
CHARACTERISTICS
A. Spatial Domain
In this section, we present the proposed methods for theimage registration of Hyperion and ASTER images and theestimation of the point spread transform matrix ( ). In general,
the spatial resolution of multispectral sensors is superior to thatof hyperspectral sensors. The ground sampling distance of an
ASTER VNIR image is 15 m whereas that of a Hyperion image
is 30 m. A template-matching technique using correlation coefficient (CC) is applied to find the corresponding coordinates
in Hyperion and ASTER images for each pixel [22]. The CCs
between the pixel with the maximum value and the neighboring
pixels are fitted to a parabola function to calculate the corresponding coordinates with subpixel order. Both the ASTER
and Hyperion images undergo warping because of satellite

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vibration, the Earth’s rotation, elevation parallax, and sensor
parallax. Accurate registration of the two images is essential
to obtain high-quality fused images [23]. In this study, some
contrivances are used in the search for corresponding points. In
regions without characteristics such as seas, lakes, and rivers,
it is difficult to find corresponding coordinates. Therefore, insuch regions, we estimate the corresponding coordinates using
affine transformations obtained from successful regions. The
coefficients of affine transformations are calculated by theleast-squares method using reliable matching coordinates. The
ASTER images are resampled and geometrically registered to
the Hyperion coordinate.
After the image registration, the point spread transform matrix
is estimated by finding the relation between the image
sharpness of two low spatial-resolution multispectral images,
i.e.,
and
. The
(
) column vector of
matrix is assumed to be a discrete approximation of a 2D
Gaussian function for the
pixel value in the hyperspectral
image. Therefore, matrix acts as a Gaussian blur filter for
down-sampling the spatial resolution of the multispectral image.
For each multispectral band, a Gaussian blur filter is estimatedon a grid for a Gaussian blur parameter by maximizing the CC
between the two Sobel images of the low spatial-resolution multispectral images obtained fromand
. The Sobel operator is a common edge detection algorithm and is used to calculate the sharpness of an image [24].
and are necessary for the estimation of and , respectively. We have empirically found that a change in the spectral
response transform matrix ( ) after the launch does not change
the sharpness of the low spatial-resolution multispectral image(). Therefore, the estimation of is first conducted using
obtained from the prelaunch sensor characteristics.
B. Spectral Domain
In this section, the proposed method for estimating the relative spectral response function using convex optimization ispresented. Let us consider
andas hyperspectral and multispectral images with the same spatial resolution, respectively. is obtained as a low-pass filteredmultispectral image by (4) using matrix . When the spectral
bandwidths of a hyperspectral sensor are extremely narrow andcover the entire spectral range of a multispectral sensor, thespectral response functions for the multispectral sensor can beapproximated as a linear combination of those for the hyperspectral sensor. In this case, the low spatial-resolution multispectral image ( ) can be assumed to be a linear combinationof the original hyperspectral images ( ) as shown in (6). The
row vector () of matrix
consist of thecoefficients of the linear combination for themultispectral
band.
which is therow andcolumn component of, denotes the contribution ratio of thehyperspectral bandto themultispectral band.
is estimated by calculating
the degree of mutual overlapping of the spectral response functions between themultispectral band and thehyperspectral band:
(11)

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 6, NO. 2, APRIL 2013

whereis the spectral response function of themultispectral band andis that of thehyperspectral band, both of which are functions of the wavelength .
The spectral response functions of satellite optical sensors canbe changed after launching [25].
is used as the prelaunch, which is obtained from (11) using the prelaunch spectral response functions. Whenis used for (6), the approximation of(6) contains large errors because of the difference between thehistograms ofand(). Foreach multispectral band,is radiometrically modified by histogram matching. Onboard estimation of the spectral response
function requires the optimization of that satisfies (6) using
the observed
and
as well as the prior knowledge of.
When we focus on the image of themultispectral band (), it is given in terms of the spectral response transform
vectoras(12)by a ratio of
When we assume that is different from(), the estimation ofboils down to the following
minimization with respect to (
):
(13)
(14)
The constraint given by (14) can be reformulated into a single
inequality as(15)
is the identity matrix. This minimizationwhere
problem requires convex optimization [26]. We used CVX for
convex optimization, which is a MATLAB-based modelingsystem [27].
IV. DATA AND EVALUATION
Hyperspectral and multispectral data fusion was applied toHyperion and ASTER images taken over San Francisco on 31
July, 2002. On that date, two sensors were in the same orbit
with a 30 min time difference (Hyperion: 10:35 am, ASTER:
11:05 am). We assumed that the observation conditions for these
two images were the same. The Hyperion/VNIR data with 30 m
ground sampling distance and 50 bands (spectral channels 8–57)
and ASTER/VNIR data with 15 m ground sampling distanceand there bands were used for the fusion of hyperspectral andmultispectral data.
The sensor characteristics (matrices and ) are estimatedusing the original datasets. To evaluate the performances of thethree data fusion techniques in Section II, two scenarios are considered.
1) In the first scenario, the original images are spatially degraded and the fused image is compared with the original
hyperspectral image.
2) In the second scenario, the quality of data fusion is evaluated by comparing hyperspectral and multispectral images
degraded from the fused image with the original images.
In the evaluation of the image-sharpening technique for realdata, the first scenario is common because many pansharpening

Fig. 1. Registered Hyperion (yellow) and ASTER (red) images.

techniques mainly focus on enhancing the resolution of multispectral images. In this scenario,
is the same as that estimated from the original datasets and is estimated from thespatially degraded datasets. The first scenario is biased toward
information in the high spectral-resolution image. There is no
guarantee that the fused image will also inherit the properties ofthe low spectral-resolution and high spatial-resolution images.
We regard hyperspectral and multispectral data fusion as not
only enhancing the spatial resolution of the hyperspectral image
but also enhancing the spectral resolution of the multispectral
image. The second scenario is adopted because it is importantthat the fused image inherits the information in both the hyperspectral and multispectral images. As a result of this evaluation,
we can determine the characteristics of the fusion methods.
The root-mean-square error (RMSE) and CC are used toevaluate the spatial reconstruction quality of each band image,and spectral angle error (SAE) is adopted for the spectral
evaluation of each pixel. To compare the pansharpening-based
methods and the unmixing-based method, the overlapping
spectral bands of the two sensors, i.e., Hyperion bands 13–24
for ASTER band 1, Hyperion bands 25–35 for ASTER band 2,
and Hyperion bands 36–53 for ASTER band 3, are used toevaluate and characterize their performances.
V. RESULTS AND DISCUSSION
A. Sensor Characteristics
The Hyperion image registered to the ASTER coordinates
is shown in Fig. 1. The difference between the actual corresponding coordinates and those estimated by affine transformation is shown in Fig. 2. Satellite vibration and sensor parallax
can be observed in Fig. 2. The coefficients of the affine transformation between ASTER band 1 and Hyperion are calculated
as

(16)

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423

Fig. 2. Residual of coordinates obtained from affine transformation (left- , right- ).

Fig. 3. Correlation coefficient of sharpness between two low spatial-resolution multispectral images generated from Hyperion and ASTER data.

Fig. 4. Effect of relative change in onboard spectral response transform matrix ( ) on cost function.

where
and
denotethe column and row coordinates of the ASTER and Hyperion
images, respectively. Fig. 3 shows the CC of the Sobel images
between two low spatial-resolution multispectral images generated from Hyperion () and ASTER () (1, 2, 3)
data. The CC is a function of the FWHM of the Gaussian blurfunction. Finally, we set the FWHM as 2.2 and 2.4 for the
first and second scenarios, respectively. This means that theHyperion image is more blurred than the official difference
between ground sampling distances of Hyperion and ASTER,
i.e., 2.
Fig. 4 shows the effect of the relative change in the onboard
spectral response transform matrix ( ) on the cost functions(). The cost functions decrease monotonically with increasing ; however, the decrease becomes gradualat approximately. Assuming that the onboard sensor
characteristics do not change significantly, the parameteris empirically set to 0.2 to allow a change of 20% from theprelaunch value. The graph on the left of Fig. 5 shows the relative spectral response between ASTER and Hyperion estimatedfrom observed images in the case of convex optimization.
Table I shows the cost functions () foreach multispectral band. For comparison, results obtained by
the simple least-squares method are also shown on the rightof Fig. 5 and in Table I. The least-squares method has theminimum cost but negative values appear in , which are notphysically meaningful. In contrast, a physically meaningful
result was obtained with the proposed method with a smaller
cost function than that for the prelaunch condition. We used
the result obtained by convex optimization for data fusion.
Further experiments using a range of scenarios will improvethe estimation accuracy of .
B. Data Fusion
The top rows of Table II show numerical evaluation results
for the three methods. SFIM gave the best results for the firstscenario and the hyperspectral-based second scenario because
the spatially degraded fused image in SFIM is the original
hyperspectral image as can be seen from (5). SSCN gave thebest result for the multispectral-based second scenario because

424IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 6, NO. 2, APRIL 2013

Fig. 5. Result of estimating relative spectral response function between ASTER and Hyperion. Prelaunch (dashed line on left), convex optimization (solid line
on left) and least-squares (right).

TABLE I
COST FUNCTIONS FOR EACH BAND

the spectrally degraded fused image in SSCN corresponds tothe original multispectral image according to (7). The assumption used in SFIM and SSCN, i.e., the radiance ratio between
high spatial-resolution hyperspectral and original multispectral
images is the same as that between original hyperspectral andlow-pass filtered multispectral images, may hold owing to thesmall difference between the spatial resolution of Hyperionand ASTER. CNMF gave intermediate results, which meansthat this method tends to inherit the characteristics of both
hyperspectral and multispectral data. When the difference inthe spatial resolution becomes so large that the assumption of
SFIM and SSCN does not hold, CNMF can be used to enhance
all hyperspectral bands with small spectral distortion owing
to unmixing. Fig. 6 shows RGB color images of the original
hyperspectral and CNMF fused data using bands 13 (467.5nm), 23 (569.3 nm), and 33 (671.0 nm) for blue, green, andred, respectively. As can be seen from the enlarged figure, theimage becomes clear, meaning that roads and buildings become
easier to discern.
The impact of the sensor characteristics on the fused image
was investigated using the prelaunch and instead of the corresponding matrices estimated by convex optimization. andare used in SSCN and SFIM, respectively. By comparing the top
and bottom rows of Table II, it can be seen that the optimizationof improves the results of SSCN for both hyperspectral andmultispectral criteria. The optimization of has a positive effecton the results of SFIM in terms of multispectral-based evaluation but a negative effect in terms of hyperspectral-based evaluation. This indicates that the accuracy of the estimated spectral
response function ( ) has a greater effect on the final productthan that of the point spread function ( ). However, the decreasein RMSE due to the optimization of the sensor characteristic estimation is much smaller than the RMSE itself, indicating that

Fig. 6. Original Hyperion (top) and CNMF fused (bottom) images (B: 467.5
nm, G: 569.3 nm, R: 671.0 nm).

the residual errors originate from the difference between two
images caused by atmospheric effects, illumination conditions,and other sensor characteristics.
VI. CONCLUSION
Hyperspectral and multispectral data fusion was applied toreal satellite hyperspectral and multispectral datasets, i.e., Hyperion and ASTER images. The preprocessing of the datasetsand the estimation of the sensor characteristics were demonstrated in detail. The estimation of the relative spectral response
function by convex optimization has a positive impact on the
final product. The cross-calibration of sensor characteristics canbe used for the data fusion of different optical images. Several
fusion techniques were used for hyperspectral and multispectral
data fusion in this study and their properties were investigated.
Pansharpening-based methods, i.e., SFIM and SSCN, tend tobe biased toward one image, while unmixing-based CNMF inherits the characteristics of both images. This paper is expected
to contribute to the practical use of hyperspectral and multispectral data fusion which will enable accurate classification with

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425

TABLE IICOMPARISONS OF AVERAGE RMSE, CC, AND SAE (IN DEGREES)

fine spatial resolution and thereby enhance the applications ofhyperspectral remote sensing.
The results of this study will be applicable to the Japanesenext-generation earth-observing satellite, Advanced Land
Observing Satellite 3 (ALOS-3), due to be launched in 2015,
which will carry the Hyperspectral Imager Suite (HISUI) opticalsensor, which is composed of hyperspectral and multispectral
sensors [28]. HISUI will allow us to obtain hyperspectral andmultispectral datasets taken over the same areas with the sameobservation conditions.
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Norimasa Mayumi received the M.Sc. degree inaerospace engineering from the University of Tokyo,
Tokyo, Japan, in 2012. He enrolled at the Universityof Tokyo in 2006, where he engaged in research on
space technology and remote-sensing systems.

Naoto Yokoya received the M.Sc. degree inaerospace engineering from the University of Tokyo,
Tokyo, Japan, in 2010. He is working towards
the Ph.D. degree in aerospace engineering at the
University of Tokyo. Since April 2012, he has
been a research fellow of the Japan Society for the
Promotion of Science.
His research interests include hyperspectral data
analysis.

Akira Iwasaki received the M.Sc. degree inaerospace engineering from the University of Tokyo,
Japan, in 1987. He joined the Electrotechnical Laboratory in 1987 where he was engaged in research on
space technology and remote sensing systems. He
received the Doctoral degree in Engineering fromthe University of Tokyo in 1996.
He is currently a Professor with the University ofTokyo.